Comparing of Term Clustering Frameworks for Modular Ontology Learning

TL;DR

This paper compares different term clustering frameworks for modular ontology learning from domain-specific text, focusing on co-occurrence matrices, various encoding methods, and clustering algorithms like K-Means and Affinity Propagation.

Contribution

It introduces and evaluates multiple term clustering frameworks, highlighting the effectiveness of Affinity Propagation and NMF encoding in ontology construction.

Findings

Affinity Propagation outperforms K-Means in clustering co-occurred terms

NMF encoding provides effective feature compression

Iterative parameter tuning improves K-Means performance

Abstract

This paper aims to use term clustering to build a modular ontology according to core ontology from domain-specific text. The acquisition of semantic knowledge focuses on noun phrase appearing with the same syntactic roles in relation to a verb or its preposition combination in a sentence. The construction of this co-occurrence matrix from context helps to build feature space of noun phrases, which is then transformed to several encoding representations including feature selection and dimensionality reduction. In addition, the content has also been presented with the construction of word vectors. These representations are clustered respectively with K-Means and Affinity Propagation (AP) methods, which differentiate into the term clustering frameworks. Due to the randomness of K-Means, iteration efforts are adopted to find the optimal parameter. The frameworks are evaluated extensively where AP shows dominant effectiveness for co-occurred terms and NMF encoding technique is salient by its promising facilities in feature compression.